1、simulated annealing

simulated annealing发音

英：　　美：

simulated annealing中文意思翻译

常用释义：模拟退火：一种随机改进算法

模拟退火法

simulated annealing双语使用场景

1、Stochastic network can employ simulated annealing method to optimize parameters of equivalent circuits.───随机网络可利用模拟退火算法优化等效电路参数.

2、The method of simulated annealing algorithm is used to solve the model.───并给出了模型求解的方法,通过模拟退火算法优化求解该模型.

3、Simulated Annealing Tools Software complete source code can be directly used by the test.───模拟退火工具软件完整的源代码可以直接使用的考验.

4、So the combination of genetic algorithm and simulated annealing could be sufficient to initial alignment for speed and precision.───因此，将遗传算法和模拟退火算法结合起来，能很好地解决初始对准的速度和精度的问题。

5、The simulated annealing algorithm is used to determine material parameters of fluid - saturated porous media response data.───本文研究了模拟退火算法在流体饱和孔隙介质参数反演中的应用.

6、To optimize sub - job assignments, two simplified algorithms of simulated annealing are proposed.───为优化子作业指派, 提出了两个简化的仿真退火算法.

7、Self - adaptative simulated annealing is selected as optimization method for this thesis.───选择自适应模拟退火算法作为本文的优化方法.

8、The dissertation designed simulated annealing algorithms and hybrid algorithms for searching parameters during inversion.───将模拟退火算法和混合算法用于反演过程中的参数寻优.

9、So, simulated annealing and simplex combine to form a global hybrid method.───因此本文将快速模拟退火和下降单纯形结合起来,形成全局混合法.

10、In this paper, we present a speculative simulated annealing algorithm for task mapping.───本文提出了一种冒险模拟退火算法.

11、The strike price and the interest rate is solved, using simulated annealing.───最后, 应用模拟退火算法,求解贴现价格、履约价格.

12、To optimize discrete volume, a stochastic solution procedure via an improved simulated annealing algorithm is presented.───通过分段积分的方法,解决了柔度的最优化问题, 从而改进了现有的模型.

13、So the simulated annealing algorithm is introduced to solving the material balance equation.───因此，本文引入模拟退火算法求解物质平衡方程。

14、Very fast simulated annealing method ( VFSA ) is used in cross dipole anisotropy inversion commonly.───正交偶极子各向异性反演中一般采用快速模拟退火算法 ( VFSA).

15、The subproblem is solved by simulated annealing algorithm.───该子问题可通过模拟退火算法来解决.

16、The simulated annealing is combined with genetic algorithm. The new tournament selection strategy is proposed.───通过将模拟退火算法嵌入到遗传算法中，建立了一种新的锦标赛选择策略。

17、This paper studies the simulated annealing algorithm for topology optimization of truss.───研究了平面桁架结构拓扑优化设计的模拟退火算法.

18、A mathematical model for ferry schedule problem is developed and solved with simulated annealing algorithm.───建立了客轮调度问题的数学模型,并用模拟退火算法求其数值解.

simulated annealing相似词语短语

1、vaulted ceiling───拱形的天花板

2、simple ordering───线性序

3、simulated leather───仿皮革

4、simulated leathers───人造革

5、simultaneous translation───同声传译；同步翻译

6、simulating───n.模拟；假装

7、self-annealing───自退火

8、simultaneous translations───同声传译；同步翻译

9、simultaneity───n.同时；[计][力]同时性；同时发生

2、最优化理论与方法的目录

第1篇线性规划与整数规划

1最优化基本要素

1.1优化变量

1.2目标函数

1.3约束条件

1.4最优化问题的数学模型及分类

1.5最优化方法概述

习题

参考文献

2线性规划

2.1线性规划数学模型

2.2线性规划求解基本原理

2.3单纯形方法

2.4初始基本可行解的获取

习题

参考文献

3整数规划

3.1整数规划数学模型及穷举法

3.2割平面法

3.3分枝定界法

习题

参考文献

第2篇非线性规划

4非线性规划数学基础

4.1多元函数的泰勒展开式

4.2函数的方向导数与最速下降方向

4.3函数的二次型与正定矩阵

4.4无约束优化的极值条件

4.5凸函数与凸规划

4.6约束优化的极值条件

习题

参考文献

5一维最优化方法

5.1搜索区间的确定

5.2黄金分割法

5.3二次插值法

5.4切线法

5.5格点法

习题

参考文献

6无约束多维非线性规划方法

6.1坐标轮换法

6.2最速下降法

6.3牛顿法

6.4变尺度法

6.5共轭方向法

6.6单纯形法

6.7最小二乘法

习题

参考文献

7约束问题的非线性规划方法

7.1约束最优化问题的间接解法

7.2约束最优化问题的直接解法

习题

参考文献

8非线性规划中的一些其他方法

8.1多目标优化

8.2数学模型的尺度变换

8.3灵敏度分析及可变容差法

习题

参考文献

第3篇智能优化方法

9启发式搜索方法

9.1图搜索算法

9.2启发式评价函数

9.3A*搜索算法

习题

参考文献

10Hopfield神经网络优化方法

10.1人工神经网络模型

10.2Hopfield神经网络

10.3Hopfield网络与最优化问题

习题

参考文献

11模拟退火法与均场退火法

11.1模拟退火法基础

11.2模拟退火算法

11.3随机型神经网络

11.4均场退火

习题

参考文献

12遗传算法

12.1遗传算法实现

12.2遗传算法示例

12.3实数编码的遗传算法

习题

参考文献

第4篇变分法与动态规划

13变分法

13.1泛函

13.2泛函极值条件——欧拉方程

13.3可动边界泛函的极值

13.4条件极值问题

13.5利用变分法求解最优控制问题

习题

参考文献

14最大（小）值原理

14.1连续系统的最大（小）值原理

14.2应用最大（小）值原理求解最优控制问题

14.3离散系统的最大（小）值原理

习题

参考文献

15动态规划

15.1动态规划数学模型与算法

15.2确定性多阶段决策

15.3动态系统最优控制问题

习题

参考文献

附录A中英文索引

Part 1Linear Programming and Integer Programming

1Fundamentals of Optimization

1.1Optimal Variables

1.2Objective Function

1.3Constraints

1.4Mathematical Model and Classification of Optimization

1.5Introduction of Optimal Methods

Problems

References

2Linear Programming

2.1Mathematical Models of Linear Programming

2.2Basic Principles of Linear Programming

2.3Simplex Method

2.4Acquirement of Initial Basic Feasible Solution

Problems

References

3Integer Programming

3.1Mathematical Models of Integer Programming and Enumeration

Method

3.2Cutting Plane Method

3.3Branch and Bound Method

Problems

References

Part 2Non?Linear Programming

4Mathematical Basis of Non?Linear Programming

4.1Taylor Expansion of Multi?Variable Function

4.2Directional Derivative of Function and Steepest Descent Direction

4.3Quadratic Form and Positive Matrix

4.4Extreme Conditions of Unconstrained Optimum

4.5Convex Function and Convex Programming

4.6Extreme Conditions of Constrained Optimum

Problems

References

5One?Dimensional Optimal Methods

5.1Determination of Search Interval

5.2Golden Section Method

5.3Quadratic Interpolation Method

5.4Tangent Method

5.5Grid Method

Problems

References

6Non?Constraint Non?Linear Programming

6.1Coordinate Alternation Method

6.2Steepest Descent Method

6.3Newton?s Method

6.4Variable Metric Method

6.5Conjugate Gradient Algorithm

6.6Simplex Method

6.7Least Squares Method

Problems

References

7Constraint Optimal Methods

7.1Constraint Optimal Indirect Methods

7.2Constraint Optimal Direct Methods

Problems

References

8Other Methods in Non Linear Programming

8.1Multi Objectives Optimazation

8.2Metric Variation of a Mathematic Model

8.3Sensitivity Analysis and Flexible Tolerance Method

Problems

References

Part 3Intelligent Optimization Method

9Heuristic Search Method

9.1Graph Search Method

9.2Heuristic Evaluation Function

9.3A*Search Method

Problems

References

10Optimization Method Based on Hopfield Neural Networks

10.1Artificial Neural Networks Model

10.2Hopfield Neural Networks

10.3Hopfield Neural Networks and Optimization Problems

Problems

References

11Simulated Annealing Algorithm and Mean Field Annealing Algorithm

11.1Basis of Simulated Annealing Algorithm

11.2Simulated Annealing Algorithm

11.3Stochastic Neural Networks

11.4Mean Field Annealing Algorithm

Problems

References

12Genetic Algorithm

12.1Implementation Procedure of Genetic Algorithm

12.2Genetic Algorithm Examples

12.3Real?Number Encoding Genetic Algorithm

Problems

References

Part 4Variation Method and Dynamic Programming

13Variation Method

13.1Functional

13.2Functional Extreme Value Condition—Euler?s Equation

13.3Functional Extreme Value for Moving Boundary

13.4Conditonal Extreme Value

13.5Solving Optimal Control with Variation Method

Problems

References

14Maximum (Minimum) Principle

14.1Maximum (Minimum) Principle for Continuum System

14.2Applications of Maximum (Minimum) Principle

14.3Maximum (Minimum) Principle for Discrete System

Problems

References

15Dynamic Programming

15.1Mathematic Model and Algorithm of Dynamic Programming

15.2Deterministic Multi?Stage Process Decision

15.3Optimal Control of Dynamic System

Problems

References

Appendix AChinese and English Index